On the other hand, machine learning can assist in the quest for understanding how brains perform computation, especially when viewed through the lens of a spiking neural network (SNN). For example, variational inference and reinforcement learning unveil SNN architectures that perform probabilistic inference consistent with STDP rules [1]}, [2]}, [3]}.
Learning-to-learn methods and Long Short-Term Memory help identify a novel SNN architecture with the capability to store long spatiotemporal sequences of spikes during computation [4]}.
Moreover, building generative models of SNNs from experimental time-series data of action potentials yields insights into the network structure generating such spikes [5]}, [6]}, [7]}.
